static void GenBufPutCriteria(GridSpace <GenStruct> .CellValue oldvalue, GenStruct newdata, Vector new_coord,
Vector cell_center, double[] cell_sizes)
{
if (newdata.birth - oldvalue.data.birth > 200)
{
new_coord.DeepCopy(oldvalue.coord);
oldvalue.data = newdata;
return;
}
if (Vector.SqrLength(oldvalue.coord, cell_center) > Vector.SqrLength(new_coord, cell_center))
{
new_coord.DeepCopy(oldvalue.coord);
oldvalue.data = newdata;
return;
}
}
public OnlineAnnTrainer(SimpleAnn ann, int imm_buf_size, int[] gen_cells, double[] l_gen_bound, double[] u_gen_bound,
Action <Vector> input_method, Func <double> output_method)
{
this.ann = ann;
// Immediate buffer initialization
imm_buf_inputs = new CircularBufferAA <Vector>(imm_buf_size, true);
imm_buf_vectors = new VectorArray(ann.input_count, imm_buf_size);
imm_training_inputs = new CircularBufferAA <Vector>(imm_buf_size, true);
imm_training_vectors = new VectorArray(ann.input_count, imm_buf_size);
imm_buf_outputs = new CircularBufferAA <double>(imm_buf_size, true);
imm_training_outputs = new CircularBufferAA <double>(imm_buf_size, true);
// bind vectors in circular buffers to vector arrays
for (int i = 0; i < imm_buf_size; i++)
{
imm_buf_inputs[i] = imm_buf_vectors[i];
imm_training_inputs[i] = imm_training_vectors[i];
}
// Generalization space initialization
gen_space = new GridSpace <GenStruct>(ann.input_count, gen_cells, l_gen_bound, u_gen_bound);
linear_gen_buff = gen_space.Linearized;
gen_space.put_method = GenBufPutCriteria;
// Delegates assignment
input_update_dlg = input_method;
output_update_dlg = output_method;
// Preallocate buffers for ann
int supercell_size = gen_cells[0];
for (int i = 1; i < ann.input_count; i++)
{
supercell_size *= gen_cells[i];
}
ann.preallocate(imm_buf_size + supercell_size);
// Misc
batch_size = imm_buf_size;
coord_vector = new Vector(ann.input_count);
}
